
from argopy import IndexFetcher, DataFetcher
import matplotlib.pyplot as plt
import cmocean as cm
from matplotlib.ticker import MaxNLocator
from matplotlib.lines import Line2D
import cartopy.crs as ccrs
plt.rcParams['axes.grid'] = True
import numpy as np
import gsw
import xarray as xr
import pandas as pd

/opt/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm


wmos = [6903075,6903076]


IndexFetcher(src='erddap',cache=False).float(wmos).plot()

(<Figure size 720x720 with 1 Axes>,
 <cartopy.mpl.geoaxes.GeoAxesSubplot at 0x7f465a1c7668>)


# GET DATA FROM REPO DATA COPY (cache=False because new profiles are added to the repo)
ds = DataFetcher(cache=False,src='localftp',local_ftp='/export/home/kbalem/Bureau/earise-rbr/data/floats/ftp',mode='expert').float(wmos).to_xarray()
# CALCULATE TEOS10 VARIABLE
ds.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
ds = ds.argo.point2profile()
ds0=ds.where(ds['PLATFORM_NUMBER']==wmos[0],drop=True) 
ds1=ds.where(ds['PLATFORM_NUMBER']==wmos[1],drop=True)


def full_profiles(ds0=ds0,ds1=ds1,markersize=1):
    fig,ax = plt.subplots(len(wmos),2,figsize=(16,len(wmos)*8),sharey=True)
    ax[0,0].invert_yaxis();
    ax[0,0].plot(ds0['TEMP'],-gsw.z_from_p(ds0['PRES'],ds0['LATITUDE'].mean()),'.k',markersize=markersize)
    ax[0,1].plot(ds0['PSAL'],-gsw.z_from_p(ds0['PRES'],ds0['LATITUDE'].mean()),'.k',markersize=markersize)    
    ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                            label='RBR Floats',markerfacecolor='k', markersize=10),
                            ],loc='upper right', bbox_to_anchor=(1.3, 1))
    ax[0,0].set_ylabel('Depth',fontsize='large',fontweight='bold')
    a=ax[0,1].text(1.05,0.4,'float '+str(wmos[0]),rotation="vertical",transform = ax[0,1].transAxes, fontsize='large', fontweight='bold')
    ax[0,0].set_xlabel('Temperature',fontsize='large',fontweight='bold')
    ax[0,1].set_xlabel('Salinity',fontsize='large',fontweight='bold')
    ax[1,0].plot(ds1['TEMP'],-gsw.z_from_p(ds1['PRES'],ds1['LATITUDE'].mean()),'.k',markersize=markersize)
    ax[1,1].plot(ds1['PSAL'],-gsw.z_from_p(ds1['PRES'],ds1['LATITUDE'].mean()),'.k',markersize=markersize)        
    ax[1,0].set_ylabel('Depth',fontsize='large',fontweight='bold')
    a=ax[1,1].text(1.05,0.4,'float '+str(wmos[1]),rotation="vertical",transform = ax[1,1].transAxes, fontsize='large', fontweight='bold')
    ax[1,0].set_xlabel('Temperature',fontsize='large',fontweight='bold')
    ax[1,1].set_xlabel('Salinity',fontsize='large',fontweight='bold')       
    ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                            label='RBR Floats',markerfacecolor='k', markersize=10),
                            ],loc='upper right', bbox_to_anchor=(1.3, 1))
    return fig,ax
fig,ax = full_profiles()


# TS DIAGRAM
def full_ts(ds0=ds0,ds1=ds1,vmin=25.95,vmax=27.8,sigma=8):    
    mint=ds['PTEMP'].min()
    maxt=ds['PTEMP'].max()
    mins=ds['SA'].min()
    maxs=ds['SA'].max()
    tempL=np.linspace(mint-1,maxt+1,156)
    salL=np.linspace(mins-1,maxs+1,156)
    Tg, Sg = np.meshgrid(tempL,salL)
    sigma_theta = gsw.sigma0(Sg, Tg)

    fig,ax=plt.subplots(1,2,figsize=(25,10))

    cs = ax[0].contour(Sg, Tg, sigma_theta,sigma, colors='grey', zorder=1)
    cl=ax[0].clabel(cs,fontsize=10,inline=False,fmt='%.1f')
    sc=ax[0].scatter(ds0['SA'],ds0['PTEMP'],c=ds0['SIG0'],s=10,cmap=cm.cm.matter,vmin=vmin,vmax=vmax)

    ax[0].set_xlabel('Salinity ($‰$)')
    ax[0].set_ylabel('Temperature[$^\circ$C]')
    ax[0].set_title(str(wmos[0]),fontsize=14, fontweight='bold')
    ax[0].xaxis.set_major_locator(MaxNLocator(nbins=6))
    ax[0].yaxis.set_major_locator(MaxNLocator(nbins=8))
    ax[0].tick_params(direction='out')

    cs = ax[1].contour(Sg, Tg, sigma_theta, sigma, colors='grey', zorder=1)
    cl=ax[1].clabel(cs,fontsize=10,inline=False,fmt='%.1f')
    sc=ax[1].scatter(ds1['SA'],ds1['PTEMP'],c=ds1['SIG0'],s=10,cmap=cm.cm.matter,vmin=vmin,vmax=vmax)

    ax[1].set_xlabel('Salinity ($‰$)')
    ax[1].set_ylabel('Temperature[$^\circ$C]')
    ax[1].set_title(str(wmos[1]),fontsize=14, fontweight='bold')
    ax[1].xaxis.set_major_locator(MaxNLocator(nbins=6))
    ax[1].yaxis.set_major_locator(MaxNLocator(nbins=8))
    ax[1].tick_params(direction='out')

    cb=plt.colorbar(sc,ax=ax.ravel().tolist())
    cb.ax.tick_params(direction='out')
    cb.set_label('Density[kg m$^{-3}$]')

    ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10)],loc='lower right')
    ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10)],loc='lower right')
    return fig,ax
fig,ax = full_ts()


# WOA OPENDAP URL FOR 2005-2017, GLOBAL 1/4°
woa_temp='https://www.ncei.noaa.gov/thredds-ocean/dodsC/ncei/woa/temperature/A5B7/0.25/woa18_A5B7_t13_04.nc'
woa_psal='https://www.ncei.noaa.gov/thredds-ocean/dodsC/ncei/woa/salinity/A5B7/0.25/woa18_A5B7_s13_04.nc'

# LOAD DATASETS, No need to decode times for a clim
woat = xr.open_dataset(woa_temp,decode_times=False)
woas = xr.open_dataset(woa_psal,decode_times=False)


# INTERPOLATE WOA T/S/SIG TO FLOAT POSITION
woat_float0 = woat['t_mn'].interp(lat=ds0['LATITUDE'],lon=ds0['LONGITUDE'],method='linear').squeeze().to_dataset()
woas_float0 = woas['s_mn'].interp(lat=ds0['LATITUDE'],lon=ds0['LONGITUDE'],method='linear').squeeze().to_dataset()
woa_float0 = xr.merge([woat_float0,woas_float0])

woat_float1 = woat['t_mn'].interp(lat=ds1['LATITUDE'],lon=ds1['LONGITUDE'],method='linear').squeeze().to_dataset()
woas_float1 = woas['s_mn'].interp(lat=ds1['LATITUDE'],lon=ds1['LONGITUDE'],method='linear').squeeze().to_dataset()
woa_float1 = xr.merge([woat_float1,woas_float1])

# Reduce depth range
woa_float0 = woa_float0.sel(depth=slice(0,2000))
woa_float1 = woa_float1.sel(depth=slice(0,2000))


fig,ax=full_profiles()
ax[0,0].plot(woa_float0['t_mn'],woa_float0['depth'],'.r',markersize=4)         
ax[0,1].plot(woa_float0['s_mn'],woa_float0['depth'],'.r',markersize=4)       
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
ax[1,0].plot(woa_float1['t_mn'],woa_float1['depth'],'.r',linewidth=1)         
ax[1,1].plot(woa_float1['s_mn'],woa_float1['depth'],'.r')       
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

<matplotlib.legend.Legend at 0x7f46426b4518>


# ZOOM
fig,ax=full_profiles(markersize=4)
ax[0,0].set_ylim(2000,1500)
ax[0,0].plot(woa_float0['t_mn'],woa_float0['depth'],'.r',markersize=8)         
ax[0,1].plot(woa_float0['s_mn'],woa_float0['depth'],'.r',markersize=8)       
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
ax[1,0].plot(woa_float1['t_mn'],woa_float1['depth'],'.r',linewidth=1)         
ax[1,1].plot(woa_float1['s_mn'],woa_float1['depth'],'.r')       
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);


# GSW 
pr0 = np.empty([len(woa_float0['depth']),len(woa_float0['N_PROF'])])
pr1 = np.empty([len(woa_float1['depth']),len(woa_float1['N_PROF'])])

for i in range(len(woa_float0['N_PROF'])):
    pr0[:,i]=gsw.p_from_z(-woa_float0['depth'],woa_float0['LATITUDE'].isel(N_PROF=i))
woa_float0['PRES']=xr.DataArray(pr0,dims=['depth','N_PROF'])   
woa_float0['SA']=xr.DataArray(gsw.SA_from_SP(woa_float0['s_mn'],woa_float0['PRES'],woa_float0['LONGITUDE'],woa_float0['LATITUDE']),dims=['depth','N_PROF'])
woa_float0['PTEMP']=xr.DataArray(gsw.pt0_from_t(woa_float0['SA'],woa_float0['t_mn'],woa_float0['PRES']),dims=['depth','N_PROF'])
woa_float0['SIG0']=xr.DataArray(gsw.sigma0(woa_float0['SA'], woa_float0['PTEMP']),dims=['depth','N_PROF'])

for i in range(len(woa_float1['N_PROF'])):
    pr0[:,i]=gsw.p_from_z(-woa_float1['depth'],woa_float1['LATITUDE'].isel(N_PROF=i))
woa_float1['PRES']=xr.DataArray(pr0,dims=['depth','N_PROF'])   
woa_float1['SA']=xr.DataArray(gsw.SA_from_SP(woa_float1['s_mn'],woa_float1['PRES'],woa_float1['LONGITUDE'],woa_float1['LATITUDE']),dims=['depth','N_PROF'])
woa_float1['PTEMP']=xr.DataArray(gsw.pt0_from_t(woa_float1['SA'],woa_float1['t_mn'],woa_float1['PRES']),dims=['depth','N_PROF'])
woa_float1['SIG0']=xr.DataArray(gsw.sigma0(woa_float1['SA'], woa_float1['PTEMP']),dims=['depth','N_PROF'])
woa_float1

<xarray.Dataset>
Dimensions:    (N_PROF: 18, depth: 67)
Coordinates:
  * N_PROF     (N_PROF) int64 0 19 20 21 22 23 24 25 ... 28 29 30 31 32 33 34 35
  * depth      (depth) float64 0.0 5.0 10.0 15.0 ... 1.9e+03 1.95e+03 2e+03
    time       float32 73.5
    lat        (N_PROF) float64 29.17 29.15 29.1 29.09 ... 29.64 30.05 30.41
    lon        (N_PROF) float64 -19.0 -19.02 -19.12 ... -18.53 -18.44 -18.55
    TIME       (N_PROF) datetime64[ns] 2020-12-11T10:25:00 ... 2021-05-21T05:...
    LATITUDE   (N_PROF) float64 29.17 29.15 29.1 29.09 ... 29.64 30.05 30.41
    LONGITUDE  (N_PROF) float64 -19.0 -19.02 -19.12 ... -18.53 -18.44 -18.55
Data variables:
    t_mn       (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan
    s_mn       (depth, N_PROF) float64 36.85 36.85 nan nan ... nan nan nan nan
    PRES       (depth, N_PROF) float64 0.0 0.0 0.0 ... 2.023e+03 2.024e+03
    SA         (depth, N_PROF) float64 37.02 37.02 nan nan ... nan nan nan nan
    PTEMP      (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan
    SIG0       (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan

array([ 0, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35])

array([   0.,    5.,   10.,   15.,   20.,   25.,   30.,   35.,   40.,   45.,
         50.,   55.,   60.,   65.,   70.,   75.,   80.,   85.,   90.,   95.,
        100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  325.,
        350.,  375.,  400.,  425.,  450.,  475.,  500.,  550.,  600.,  650.,
        700.,  750.,  800.,  850.,  900.,  950., 1000., 1050., 1100., 1150.,
       1200., 1250., 1300., 1350., 1400., 1450., 1500., 1550., 1600., 1650.,
       1700., 1750., 1800., 1850., 1900., 1950., 2000.])

array(73.5, dtype=float32)

array([29.16698167, 29.14661667, 29.10236333, 29.09201167, 29.05589833,
       29.16621833, 29.403165  , 29.61847333, 29.87339   , 30.188525  ,
       30.25565833, 30.11950667, 29.839565  , 29.52730333, 29.397985  ,
       29.63578667, 30.05389167, 30.41144667])

array([-19.00256167, -19.01972333, -19.11748333, -19.19157167,
       -19.21113333, -19.24770667, -19.31360333, -19.471775  ,
       -19.623935  , -19.645155  , -19.76130833, -19.79596   ,
       -19.675635  , -19.46817667, -18.98621167, -18.52723167,
       -18.43598333, -18.54520833])

array(['2020-12-11T10:25:00.000000000', '2020-12-12T05:42:20.000000000',
       '2020-12-22T05:41:20.000000000', '2021-01-01T05:44:20.000000000',
       '2021-01-11T05:30:20.000000000', '2021-01-21T05:26:20.000000000',
       '2021-01-31T05:33:20.000000000', '2021-02-10T05:39:20.000000000',
       '2021-02-20T05:51:20.000000000', '2021-03-02T05:49:20.000000000',
       '2021-03-12T05:39:20.000000000', '2021-03-22T05:32:20.000000256',
       '2021-03-31T15:29:20.000000000', '2021-04-11T05:46:20.000000000',
       '2021-04-21T05:43:20.000000000', '2021-05-01T05:36:20.000000000',
       '2021-05-11T05:34:20.000000000', '2021-05-21T05:33:20.000000000'],
      dtype='datetime64[ns]')

array([29.16698167, 29.14661667, 29.10236333, 29.09201167, 29.05589833,
       29.16621833, 29.403165  , 29.61847333, 29.87339   , 30.188525  ,
       30.25565833, 30.11950667, 29.839565  , 29.52730333, 29.397985  ,
       29.63578667, 30.05389167, 30.41144667])

array([-19.00256167, -19.01972333, -19.11748333, -19.19157167,
       -19.21113333, -19.24770667, -19.31360333, -19.471775  ,
       -19.623935  , -19.645155  , -19.76130833, -19.79596   ,
       -19.675635  , -19.46817667, -18.98621167, -18.52723167,
       -18.43598333, -18.54520833])

array([[        nan,         nan,         nan, ..., 19.56032091,
                nan, 19.01039807],
       [        nan,         nan,         nan, ..., 19.27313214,
                nan, 18.97005256],
       [19.90413882, 19.92035109,         nan, ..., 19.25128783,
                nan, 18.95651843],
       ...,
       [ 4.61089416,  4.60302342,  4.59258237, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])


# TS DIAGRAM
fig,ax = full_ts()
sc_b=ax[0].scatter(woa_float0['SA'],woa_float0['PTEMP'],c=woa_float0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k')
sc_b=ax[1].scatter(woa_float1['SA'],woa_float1['PTEMP'],c=woa_float1['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k')

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f4635418d30>


# ZOOM
fig,ax = full_ts(vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(woa_float0['SA'],woa_float0['PTEMP'],c=woa_float0['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k')
sc_b=ax[1].scatter(woa_float1['SA'],woa_float1['PTEMP'],c=woa_float1['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k')

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f46351951d0>


plt.figure()
# FETCH PROFILES META IN OUR REGION
IndexFetcher(cache=True,src='erddap').region([-19.8,-18.4,29.0,30.3]).plot()
# RBR PROFILES
plt.plot(ds['LONGITUDE'],ds['LATITUDE'],'k*',markersize=10)

[<matplotlib.lines.Line2D at 0x7fbaea985780>]

<Figure size 432x288 with 0 Axes>


# GET DATA FROM ERDDAP, in standard mode to automatically keep good data
px = DataFetcher(cache=True,src='erddap',mode='standard').region([-19.8,-18.4,29.0,30.3,0.,2000.]).to_xarray()
# CALCULATE TEOS10 VARIABLE
px.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
px = px.argo.point2profile()
# Keep only december to may profiles
px['MONTH']=xr.DataArray(np.array(pd.DatetimeIndex(px['TIME'].values).month),dims='N_PROF')
px = px.where((px['MONTH']<=5)|(px['MONTH']==12), drop=True)
px

<xarray.Dataset>
Dimensions:                (N_LEVELS: 1018, N_PROF: 109)
Coordinates:
  * N_PROF                 (N_PROF) int64 2 3 15 16 17 ... 191 192 193 194 195
    TIME                   (N_PROF) datetime64[ns] 2002-05-14T05:00:00 ... 20...
  * N_LEVELS               (N_LEVELS) int64 0 1 2 3 4 ... 1014 1015 1016 1017
    LONGITUDE              (N_PROF) float64 -19.3 -19.23 ... -18.96 -18.65
    LATITUDE               (N_PROF) float64 29.08 29.31 30.05 ... 29.58 29.95
Data variables:
    CONFIG_MISSION_NUMBER  (N_PROF) float64 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0
    CYCLE_NUMBER           (N_PROF) float64 5.0 6.0 58.0 59.0 ... 14.0 15.0 16.0
    DATA_MODE              (N_PROF) object 'D' 'D' 'D' 'D' ... 'R' 'R' 'R' 'R'
    DIRECTION              (N_PROF) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER        (N_PROF) float64 1.9e+06 1.9e+06 ... 6.903e+06
    POSITION_QC            (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    PRES                   (N_PROF, N_LEVELS) float32 15.0 25.0 35.0 ... nan nan
    PRES_QC                (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    PSAL                   (N_PROF, N_LEVELS) float32 36.873 36.869 ... nan nan
    PSAL_QC                (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    PTEMP                  (N_PROF, N_LEVELS) float64 19.41 19.41 ... nan nan
    SA                     (N_PROF, N_LEVELS) float64 37.05 37.04 ... nan nan
    SIG0                   (N_PROF, N_LEVELS) float64 26.35 26.35 ... nan nan
    TEMP                   (N_PROF, N_LEVELS) float32 19.41 19.41 ... nan nan
    TEMP_QC                (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    TIME_QC                (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    MONTH                  (N_PROF) float64 5.0 5.0 2.0 3.0 ... 4.0 4.0 5.0 5.0
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://www.ifremer.fr/erddap
    Fetched_by:           kbalem
    Fetched_date:         2021/05/21
    Fetched_constraints:  [x=-19.80/-18.40; y=29.00/30.30; z=0.0/2000.0]
    Fetched_uri:          ['https://www.ifremer.fr/erddap/tabledap/ArgoFloats...
    history:              Variables filtered according to DATA_MODE; Variable...

array([  2,   3,  15,  16,  17,  18,  19,  20,  22,  23,  27,  48,  49,  50,
        51,  52,  53,  54,  55,  56,  68,  69,  70,  71,  72,  73,  87,  88,
        89,  77,  90,  78,  79,  80,  57,  91,  92,  93, 100, 101, 102, 103,
       104, 105, 106,  30, 107, 108,  31,  58,  32, 109, 110,  94,  95,  96,
        97,  98,  99, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 175,
       176,   1,  33,  34,  35,  36,  37,  38,  39,  40,  41, 157, 158, 159,
       160, 161, 162, 163, 164, 165, 142, 143, 144, 145,   0, 182, 183, 184,
       185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195])

array(['2002-05-14T05:00:00.000000000', '2002-05-24T05:06:00.000000000',
       '2006-02-21T03:44:00.000000000', '2006-03-03T03:49:00.000000000',
       '2006-03-13T03:30:00.000000000', '2006-03-23T03:42:00.000000000',
       '2006-04-02T03:29:00.000000000', '2006-04-12T03:35:00.000000000',
       '2006-12-22T12:27:44.000000000', '2007-01-01T13:16:36.000000000',
       '2008-02-14T11:58:03.000000000', '2009-12-06T12:20:05.000000000',
       '2009-12-16T11:46:47.000000000', '2009-12-26T11:16:17.000000000',
       '2010-04-05T11:10:33.000000000', '2010-04-15T11:48:59.000000000',
       '2011-04-20T10:48:47.000000000', '2011-04-30T11:33:42.000000000',
       '2011-05-10T11:25:36.000000000', '2011-05-20T10:51:05.000000000',
       '2011-12-06T00:58:02.000000000', '2011-12-21T02:20:24.000000000',
       '2012-12-14T22:52:24.000000000', '2012-12-29T23:17:55.000000000',
       '2013-01-14T02:29:24.000000000', '2013-01-28T23:26:14.000000000',
       '2013-04-23T08:57:47.000000000', '2014-02-17T08:53:45.000000000',
       '2014-02-27T06:52:43.000000000', '2014-03-15T14:21:36.000000000',
       '2014-04-08T06:49:14.000000000', '2014-04-14T10:45:41.000000000',
       '2014-04-24T14:21:59.000000000', '2014-05-04T11:45:41.000000000',
       '2014-05-07T23:56:59.000000000', '2014-05-08T08:52:57.000000000',
       '2014-05-18T07:06:29.000000000', '2014-05-28T08:52:23.000000000',
       '2015-03-31T05:53:00.000000000', '2015-04-05T05:50:00.000000000',
...
       '2017-12-16T19:33:00.000000000', '2018-04-11T17:49:00.000000000',
       '2018-04-14T11:47:40.000000000', '2018-04-17T11:55:40.000000000',
       '2018-04-20T11:49:40.000000000', '2018-04-23T11:54:40.000000000',
       '2018-04-26T12:02:40.000000000', '2018-04-29T11:58:40.000000000',
       '2018-05-02T11:54:40.000000000', '2018-05-05T11:57:40.000000000',
       '2018-05-07T01:39:23.000000000', '2018-12-24T05:41:00.000000000',
       '2019-01-03T05:40:00.000000000', '2019-01-13T05:34:00.000000000',
       '2019-01-23T05:39:00.000000000', '2019-02-02T05:33:00.000000000',
       '2019-02-12T05:36:00.000000000', '2019-02-22T05:29:00.000000000',
       '2019-03-04T05:32:00.000000000', '2019-03-14T05:36:00.000000000',
       '2020-04-05T05:09:00.000000000', '2020-04-15T05:18:00.000000000',
       '2020-04-25T05:37:00.000000000', '2020-05-25T05:29:00.000000000',
       '2020-12-10T18:26:00.000000000', '2020-12-12T05:26:30.000000000',
       '2020-12-22T05:27:30.000000000', '2021-01-01T05:33:30.000000000',
       '2021-01-11T05:37:30.000000000', '2021-01-21T05:34:30.000000000',
       '2021-01-31T05:26:30.000000000', '2021-02-10T05:37:30.000000000',
       '2021-02-20T05:30:30.000000000', '2021-03-02T05:38:30.000000000',
       '2021-03-12T05:33:30.000000000', '2021-04-11T05:38:30.000000000',
       '2021-04-21T05:42:30.000000000', '2021-05-01T05:46:30.000000000',
       '2021-05-11T05:45:30.000000000'], dtype='datetime64[ns]')

array([   0,    1,    2, ..., 1015, 1016, 1017])

array([-19.302     , -19.235     , -19.613     , -19.307     ,
       -18.87      , -18.798     , -18.725     , -18.602     ,
       -19.47299957, -19.71800041, -19.45499992, -18.62999916,
       -19.03000069, -19.31200027, -19.1779995 , -18.61000061,
       -19.153     , -18.887     , -18.763     , -18.737     ,
       -19.259     , -19.62      , -19.481     , -19.483     ,
       -19.414     , -18.873     , -19.781     , -19.064     ,
       -19.575     , -19.538     , -19.541     , -19.502     ,
       -19.575     , -19.743     , -18.471     , -19.386     ,
       -19.578     , -19.72      , -18.463065  , -18.49731167,
       -18.50046333, -18.57160667, -18.65185167, -18.78480167,
       -18.87676667, -18.855     , -18.92749667, -18.95606   ,
       -18.978     , -18.666     , -19.464     , -18.93842167,
       -18.99732   , -18.4859    , -18.7544    , -18.9591    ,
       -19.0942    , -19.1418    , -19.1425    , -19.26692167,
       -19.21086167, -19.26507   , -19.33983   , -19.43046333,
       -19.55742167, -19.752885  , -19.76117833, -19.57670167,
       -19.56884   , -18.867     , -19.522     , -18.49936833,
       -18.48464167, -18.45897167, -18.44397   , -18.42888167,
       -18.41819   , -18.40956333, -18.40222167, -18.40022   ,
       -19.7348    , -18.942     , -18.952     , -19.104     ,
       -19.234     , -19.266     , -19.164     , -19.009     ,
       -19.011     , -19.095     , -18.501     , -18.917     ,
       -19.45      , -19.436     , -19.00882667, -19.00287833,
       -19.10077167, -19.18482   , -19.22271333, -19.26173167,
       -19.32334333, -19.46715667, -19.65580833, -19.75253833,
       -19.77647   , -19.75431167, -19.47863   , -18.96217   ,
       -18.65334   ])

array([29.08      , 29.311     , 30.046     , 30.152     , 29.784     ,
       29.476     , 29.43      , 29.743     , 29.99500084, 29.75900078,
       30.20899963, 30.24500084, 30.28800011, 30.28300095, 30.16799927,
       29.94599915, 30.165     , 30.019     , 29.862     , 29.637     ,
       29.981     , 30.18      , 30.294     , 29.499     , 29.401     ,
       29.631     , 30.23      , 29.065     , 29.484     , 29.106     ,
       30.113     , 29.858     , 29.399     , 29.099     , 30.253     ,
       30.258     , 30.221     , 30.24      , 29.36645667, 29.35907333,
       29.3141    , 29.25226833, 29.23712167, 29.25764667, 29.20940333,
       29.877     , 29.11013167, 29.02015   , 29.482     , 30.217     ,
       29.468     , 29.14659167, 29.30861   , 29.2011    , 29.1694    ,
       29.2527    , 29.2881    , 29.2294    , 29.0604    , 30.24215333,
       30.08608167, 29.84341667, 29.61126833, 29.47364   , 29.38462   ,
       29.34008   , 29.695575  , 29.45801667, 29.1975    , 30.092     ,
       30.101     , 29.15595667, 29.1382    , 29.13259667, 29.1364    ,
       29.12838167, 29.12056833, 29.10823167, 29.12612667, 29.13557833,
       29.19438   , 30.251     , 29.985     , 29.671     , 29.335     ,
       29.124     , 29.06      , 29.162     , 29.496     , 29.947     ,
       29.439     , 29.401     , 29.711     , 29.441     , 29.165725  ,
       29.150605  , 29.13182833, 29.12126333, 29.09465167, 29.18438   ,
       29.42158667, 29.65736833, 29.91031   , 30.14338667, 30.22611333,
       29.670635  , 29.50829   , 29.58076333, 29.95490167])

array([ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
       -1., -1., -1., -1., -1., -1., -1., -1.,  1., -1., -1., -1.,  4.,
        4.,  4.,  4.,  4.,  4.,  4.,  1.,  4.,  4.,  1.,  1.,  1.,  4.,
        4., 63., 55., 61., 55., 61., 64.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  2.,  2.,  1.,  1.,  2.,  2.,  2.,  2.,  2.,
        2.,  2.,  5.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  3.,
        3.,  3.,  3.,  1.,  1.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,
        2.,  2.,  2.,  2.,  2.])

array([  5.,   6.,  58.,  59.,  60.,  61.,  62.,  63.,   6.,   7.,  14.,
       118., 119., 120., 130., 131., 168., 169., 170., 171., 113., 114.,
       138., 139., 140., 141.,  20.,  50.,  51.,  90.,  55.,  93.,  94.,
        95., 172.,  58.,  59.,  60.,   6.,   7.,   8.,   9.,  10.,  11.,
        12., 182.,  13.,  14., 183., 197., 184.,  17.,  18., 124., 125.,
       126., 127., 128., 129.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,
        22.,  23.,  24.,  83.,  84.,   1.,   1.,   2.,   3.,   4.,   5.,
         6.,   7.,   8., 174.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,
        28.,  29.,  57.,  58.,  59.,  62.,   1.,   1.,   2.,   3.,   4.,
         5.,   6.,   7.,   8.,   9.,  10.,  13.,  14.,  15.,  16.])

array(['D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D',
       'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D',
       'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D',
       'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D',
       'D', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'D', 'D', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'D', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',
       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',
       'R', 'R', 'R', 'R', 'R'], dtype=object)

array(['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'D', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'D', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A'], dtype=object)


fig,ax = full_profiles()

ax[0,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[0,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
 
ax[1,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)   
ax[1,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

<matplotlib.legend.Legend at 0x7fbaec82cb70>


fig,ax = full_profiles(markersize=4)
ax[0,0].set_ylim(2000,1500)

ax[0,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[0,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
 
ax[1,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)   
ax[1,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
   
ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);


# TS DIAGRAM
fig,ax = full_ts()
sc_b=ax[0].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',zorder=0)
sc_b=ax[1].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',zorder=0)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f4627b77ef0>


# TS DIAGRAM
fig,ax = full_ts(vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',zorder=0)
sc_b=ax[1].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',zorder=0)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f46278f92b0>


st25=pd.read_csv('../data/ref/RAPROCAN2012/Station25.csv',header=4,sep=';',names=['PRES','PSAL','TEMP'])
mt25={'latitude':29.17,'longitude':-19.00,'time':np.datetime64('2020-12-10T17:41:05')}


st25['SA'] = gsw.SA_from_SP(st25['PSAL'],st25['PRES'],mt25['longitude'],mt25['latitude'])
st25['PTEMP'] = gsw.pt0_from_t(st25['SA'],st25['TEMP'],st25['PRES'])
st25['SIG0'] = gsw.sigma0(st25['SA'], st25['PTEMP'])
st25.head()


# LET's KEEP ONLY THE 1st PROFILES
ds0b=ds0.isel(N_PROF=[0,1,2,3])
ds1b=ds1.isel(N_PROF=[0,1,2,3])

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b)
ax[0,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[0,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
ax[1,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[1,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

<matplotlib.legend.Legend at 0x7f462cefb160>


fig,ax = full_profiles(ds0=ds0b,ds1=ds1b,markersize=4)

ax[0,0].set_ylim([2000,1500]); 
ax[0,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[0,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
ax[1,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[1,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b)
sc_b=ax[0].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f46296cce10>


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b,vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f46294bcbe0>


# GET DATA FROM REPO DATA COPY (cache=False because new profiles are added to the repo)
dsbe = DataFetcher(cache=False,src='erddap',mode='expert').float(6903010).to_xarray()
# CALCULATE TEOS10 VARIABLE
dsbe.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
dsbe = dsbe.argo.point2profile()
dsbe

<xarray.Dataset>
Dimensions:                   (N_LEVELS: 1018, N_PROF: 18)
Coordinates:
  * N_PROF                    (N_PROF) int64 0 1 2 3 4 5 6 ... 12 13 14 15 16 17
    TIME                      (N_PROF) datetime64[ns] 2020-12-10T18:26:00 ......
  * N_LEVELS                  (N_LEVELS) int64 0 1 2 3 4 ... 1014 1015 1016 1017
    LATITUDE                  (N_PROF) float64 29.17 29.15 29.13 ... 29.95 30.47
    LONGITUDE                 (N_PROF) float64 -19.01 -19.0 ... -18.65 -18.74
Data variables:
    CONFIG_MISSION_NUMBER     (N_PROF) int64 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
    CYCLE_NUMBER              (N_PROF) int64 1 1 2 3 4 5 6 ... 12 13 14 15 16 17
    DATA_MODE                 (N_PROF) <U1 'R' 'R' 'R' 'R' ... 'R' 'R' 'R' 'R'
    DIRECTION                 (N_PROF) <U1 'D' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER           (N_PROF) int64 6903010 6903010 ... 6903010 6903010
    POSITION_QC               (N_PROF) int64 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
    PRES                      (N_PROF, N_LEVELS) float32 8.7 10.4 ... nan nan
    PRES_ADJUSTED             (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    PRES_ADJUSTED_ERROR       (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    PRES_ADJUSTED_QC          (N_PROF) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    PRES_QC                   (N_PROF) int64 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
    PSAL                      (N_PROF, N_LEVELS) float32 36.946 36.947 ... nan
    PSAL_ADJUSTED             (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    PSAL_ADJUSTED_ERROR       (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    PSAL_ADJUSTED_QC          (N_PROF) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    PSAL_QC                   (N_PROF, N_LEVELS) int64 1 1 1 ... 99999 99999
    PTEMP                     (N_PROF, N_LEVELS) float64 21.32 21.32 ... nan nan
    SA                        (N_PROF, N_LEVELS) float64 37.12 37.12 ... nan nan
    SIG0                      (N_PROF, N_LEVELS) float64 25.89 25.89 ... nan nan
    TEMP                      (N_PROF, N_LEVELS) float32 21.32 21.325 ... nan
    TEMP_ADJUSTED             (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    TEMP_ADJUSTED_ERROR       (N_PROF, N_LEVELS) float32 nan nan nan ... nan nan
    TEMP_ADJUSTED_QC          (N_PROF) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    TEMP_QC                   (N_PROF, N_LEVELS) int64 1 1 1 ... 99999 99999
    TIME_QC                   (N_PROF) int64 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
    VERTICAL_SAMPLING_SCHEME  (N_PROF) <U207 'Primary sampling: averaged [10 ...
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://www.ifremer.fr/erddap
    Fetched_by:           kbalem
    Fetched_date:         2021/05/21
    Fetched_constraints:  phy;WMO6903010
    Fetched_uri:          ['https://www.ifremer.fr/erddap/tabledap/ArgoFloats...
    history:              Transformed with point2profile

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17])

array(['2020-12-10T18:26:00.000000000', '2020-12-12T05:26:30.000000000',
       '2020-12-22T05:27:30.000000000', '2021-01-01T05:33:30.000000000',
       '2021-01-11T05:37:30.000000000', '2021-01-21T05:34:30.000000000',
       '2021-01-31T05:26:30.000000000', '2021-02-10T05:37:30.000000000',
       '2021-02-20T05:30:30.000000000', '2021-03-02T05:38:30.000000000',
       '2021-03-12T05:33:30.000000000', '2021-03-22T05:25:30.000000000',
       '2021-04-01T05:35:30.000000000', '2021-04-11T05:38:30.000000000',
       '2021-04-21T05:42:30.000000000', '2021-05-01T05:46:30.000000000',
       '2021-05-11T05:45:30.000000000', '2021-05-21T05:47:30.000000000'],
      dtype='datetime64[ns]')

array([   0,    1,    2, ..., 1015, 1016, 1017])

array([29.165725  , 29.150605  , 29.13182833, 29.12126333, 29.09465167,
       29.18438   , 29.42158667, 29.65736833, 29.91031   , 30.14338667,
       30.22611333, 30.13541333, 29.86356   , 29.670635  , 29.50829   ,
       29.58076333, 29.95490167, 30.46821667])

array([-19.00882667, -19.00287833, -19.10077167, -19.18482   ,
       -19.22271333, -19.26173167, -19.32334333, -19.46715667,
       -19.65580833, -19.75253833, -19.77647   , -19.868995  ,
       -19.82508833, -19.75431167, -19.47863   , -18.96217   ,
       -18.65334   , -18.73503833])

array([1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

array([ 1,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17])

array(['R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',
       'R', 'R', 'R', 'R', 'R'], dtype='<U1')

array(['D', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A'], dtype='<U1')


fig=plt.figure(figsize=(6,6))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
plt.plot(dsbe['LONGITUDE'],dsbe['LATITUDE'],'ro-')
plt.plot(ds0['LONGITUDE'],ds0['LATITUDE'],'bo-')
plt.plot(ds1['LONGITUDE'],ds1['LATITUDE'],'go-')

[<matplotlib.lines.Line2D at 0x7f46293005c0>]


ds0b=ds0.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])
ds1b=ds1.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])
dsbe0=dsbe.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b)
ax[0,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
ax[0,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[1,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
ax[1,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
    
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

<matplotlib.legend.Legend at 0x7f4629dcde80>


# ZOOM
fig,ax = full_profiles(ds0=ds0b,ds1=ds1b,markersize=4)

ax[0,0].set_ylim([2000,1500]); 
ax[0,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
ax[0,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[1,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
ax[1,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
    
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b)
sc_b=ax[0].scatter(dsbe0['SA'],dsbe0['PTEMP'],c=dsbe0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(dsbe0['SA'],dsbe0['PTEMP'],c=dsbe0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f4612dfd5f8>


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b,vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)

sc_b=ax[0].scatter(ds1b['SA'],ds1b['PTEMP'],c=ds1b['SIG0'],s=10,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(ds1b['SA'],ds1b['PTEMP'],c=ds1b['SIG0'],s=10,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

<matplotlib.legend.Legend at 0x7f4612b7c828>


def along_isot(isotherm):

    fig,ax = plt.subplots(2,1,figsize=(18,12),sharex=True)
    ds0_ai = ds0.where((ds0['PTEMP']<isotherm+.1)&(ds0['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    ds1_ai = ds1.where((ds1['PTEMP']<isotherm+.1)&(ds1['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    dsbe_ai = dsbe.where((dsbe['PTEMP']<isotherm+.1)&(dsbe['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    ax[0].plot(ds0_ai['CYCLE_NUMBER'],ds0_ai['PRES'],'o-',label=str(wmos[0]))
    ax[0].plot(ds1_ai['CYCLE_NUMBER'],ds1_ai['PRES'],'o-',label=str(wmos[1]))
    ax[0].plot(dsbe_ai['CYCLE_NUMBER'],dsbe_ai['PRES'],'o-',label='sbe 6903010')
    #ax[0].set_xlabel('CYCLE')
    ax[0].set_ylabel('PRES')
    ax[0].legend()
    ax[0].set_title('Pressure along isotherm '+str(isotherm)+'°C')

    ax[1].plot(ds0_ai['CYCLE_NUMBER'],ds0_ai['SA'],'o-',label=str(wmos[0]))
    ax[1].plot(ds1_ai['CYCLE_NUMBER'],ds1_ai['SA'],'o-',label=str(wmos[1]))
    ax[1].plot(dsbe_ai['CYCLE_NUMBER'],dsbe_ai['SA'],'o-',label='sbe 6903010')
    ax[1].set_xlabel('CYCLE')
    ax[1].set_ylabel('Salinity')
    ax[1].legend()
    ax[1].set_title('Salinity along isotherm '+str(isotherm)+'°C')
    return fig,ax


for i in [15.,12.5,10.,7.5,5.]:
    fig,ax = along_isot(i)

/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)
/opt/anaconda3/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)

	PRES	PSAL	TEMP	SA	PTEMP	SIG0
0	8.0	36.9540	21.3449	37.128475	21.343339	25.870281
1	10.0	36.9541	21.3478	37.128578	21.345849	25.869658
2	12.0	36.9539	21.3508	37.128373	21.348458	25.868777
3	14.0	36.9539	21.3484	37.128369	21.345668	25.869552
4	16.0	36.9543	21.3442	37.128767	21.341078	25.871130

This is a notebook to analyse french arvor-rbr floats first data¶

Data from those floats are automatically downloaded in the repo by a github action, so we can use localftp source from argopy to check the data.¶

Let's plot TS diagram for all profiles¶

Comparison with woa¶

Let's try adding woa interpolation in TS diagram¶

Comparison with argo profiles nearby¶

Comparison of 1sts profiles with RAPROCEAN2012 CTD Cast¶

SBE 6903010 float comparison¶

Evolution of pres/salinity along an isotherm¶